• Àüü
  • ÀüÀÚ/Àü±â
  • Åë½Å
  • ÄÄÇ»ÅÍ
´Ý±â

»çÀÌÆ®¸Ê

Loading..

Please wait....

¿µ¹® ³í¹®Áö

Ȩ Ȩ > ¿¬±¸¹®Çå > ¿µ¹® ³í¹®Áö > TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) Understanding Watching Patterns of Live TV Programs on Mobile Devices: A Content Centric Perspective
¿µ¹®Á¦¸ñ(English Title) Understanding Watching Patterns of Live TV Programs on Mobile Devices: A Content Centric Perspective
ÀúÀÚ(Author) Yuheng Li   Qianchuan Zhao  
¿ø¹®¼ö·Ïó(Citation) VOL 09 NO. 09 PP. 3635 ~ 3654 (2015. 09)
Çѱ۳»¿ë
(Korean Abstract)
¿µ¹®³»¿ë
(English Abstract)
With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naïive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.


Å°¿öµå(Keyword) Mobile video streaming   network measurement   human factor   traffic estimation  
ÆÄÀÏ÷ºÎ PDF ´Ù¿î·Îµå